In view of the digital transformation that is observed, I consider that future passes, no doubt, by Artificial Intelligence (AI).

There will be more, and better artificial intelligence.

Paulo Cortez. EPMQ/UMinho

What just happened in Artifical Intelligence?

How come suddenly we have citizens, companies, televisions, politicians, a whole world talking about Artificial Intelligence?

There are lots of articles on AI and discussions of pros and cons. Everybody hears about something that was already done in the universities for many decades, only people did not know in concrete what. There were only the movies, always with an element of malevolence that AI would end the human race…

The reality of this AI explosion phenomenon is that there have recently been three major key factors that are associated with each other.

1. There has been a growth in data.

The technologies have developed in a way that it is very easy to collect data, from IoT to industry 4.0, to smart cities. The data is growing exponentially, and many times the applicability of the data is unknown. They simply are collected because it is cheap and it is easy to do it. There is a potential in the data that can be explored efficiently.

2. Computational power is growing.

Moore’s law tells us that every two years, computational power doubles. We can process more data than we currently collect.

3. There have been developments on the AI side, namely on Machine Learning (ML).

There has been a growth of the ML algorithms, the deep learning, that manages to learn complex things with big data, becoming a state of the art machine.

These 3 factors together have brought tremendous advances in AI that we now witness.

What is Artificial Intelligence?

In a very simple equation, the AI = data + algorithms.

We can have better AI if we have better data, or if we have better algorithms, and we can work on one or the other.

Over the decades we have had many subareas within AI. The ML is a subarea, data science and deep learning are others, for example. There are several AI communities that use different paradigms and try to solve distinct problems, but with those 3 main developments (having lots of data, higher computational power, more sophisticated algorithms), AI has suddenly hit mainstream applications.

Gartner Analytic Ascendancy Model

The Gartner Analytic Ascendancy Model is a scale model where the potential value that can be brought to a person, organization, or industry is raised from one stage to another. It shows you different things you can do with your data.

Descriptive analytics – understand what happened;

Diagnostic analytics – why it happened;

Predictive analytics – understand what will happen;

Prescriptive analytics – try to make things happen.

This model can be answered with many AI techniques.

We already have multiple AI and ML applications running in the real world:

the detection of plagiarism in papers,

the detection of fraud in financial transactions,

social networking apps that show the dog breed more like the person’s face,

the purchasing recommendation systems, etc.

In the sphere of EPMQ, what is happening? We are already involved in 5 projects with AI components and we realize that the intelligence needs of companies are growing. This translates to the hype in AI that is happening worldwide.

Artificial Intelligence/Machine Learning limitations

Despite all the advances, AI still has some limitations. AI has immense potential, but it is not perfect.

Several applications may benefit from having AI, but in many cases, AI is still not smart enough. As the Portuguese expert of ML Pedro Domingos says, the real problem is not having smart computers to regulate the world, but having stupid computers to regulate the world without any control.

The Future of Artificial Intelligence: EPMQ predictions

My prediction for the EPMQ in the area of artificial intelligence is more and better intelligence. More applications in more domains.

There is a whole potential where these techniques can be used. From the moment we collect more data we can analyze these data, and see if there is a potential application to support the decision or understanding of certain types of phenomena so that it helps in a given context.

AI can help solve the great challenges of humanity: eliminate poverty, do better agriculture, better understand the problem of climate change, make a more sustainable development, improve the industry, etc. And there is already this awareness at the political level, which shows the strength of this AI wave. In Portugal, this is already being used, but it is still not enough, therefore, “more intelligence“.

“Better intelligence” is the way intelligence is made. In the “DTx” collaborative project, for example, on the Gartner Analytic Ascendancy Model layer, we are trying to introduce greater complexity, which is to have evolutionary systems, systems that learn from the error, and for that we need feedback.

If we can have big data, predictive models, ways to optimize and record our decisions, we can then measure their outcome, and on that basis make an adjustment of these various components.

For intelligence, we have to have data and the data has to be well organized and structured in big data.

Machine Learning EPMQ

The process of implementing big data is time-consuming. Making ML is time-consuming. Integrating both requires a series of analyzes, etc. In 6 years, with the accumulated experience of different projects, we will be able to develop methodologies that facilitate the development of new projects.

One area that is also growing is the auto ML, making automatic ML. At its core is to push a button, having a set of data and an ML template that already serves the organization.

The scalable ML consists of making better use of computational resources. The ML has to move towards greater transparency, interpretation, and explanation. Prescriptive analytics is still not much explored. A simpler system is used. With a modern exploration, with wider use of modern optimization techniques to make things happen, thousands of viable alternatives are achieved.

Finally, the model deployment, the CCG in partnership with an organization develops an AI model, for example, extremely interesting, with high predictive capacity, but the project ends and the company needs to use this model. The transition from the model to the company demands from the methodological point of view some kind of care. This is what will be developed in this domain.

“Future Applications of EPMQ Technologies (A Vision – 2025)” is a presentation of Paulo Cortez. Associate Professor with Aggregation at ISD of UMinho and EPMQ Researcher.